The epidemiology of onychomycosis: possible role of smoking and peripheral arterial disease
Bibliographic record
Abstract
BACKGROUND: Onychomycosis is a relatively common condition the aetiology of which appears to be multifactorial, with both genetic and acquired factors being responsible. In our clinical practice we have observed that smokers and individuals with peripheral arterial disease may have an increased prevalence of onychomycosis compared to normal individuals. PATIENTS AND METHODS: Patients attending a vascular clinic in a hospital were asked about a history of smoking and peripheral arterial disease. The lower extremity was assessed for peripheral arterial disease. Material was obtained from toenails for mycological evaluation. RESULTS: Two hundred and fifty-four patients (male 146, female 108; age, mean +/- standard error, 66.6 +/- 0.8 years) were enrolled. Abnormal-appearing nails and onychomycosis were present in 49.2% and 22.4% of patients, respectively. Factors associated with onychomycosis included, increasing age (risk odds ratio [ROR] 1.05, P = 0.002), male gender (ROR 1.7, P = 0.09), smoking (packs per day) (ROR 1.9, P = 0.02) and peripheral arterial disease (ROR 4.8, P = 0.02). CONCLUSIONS: The factors predisposing to the development of onychomycosis are multifactorial. Both smoking (number of packs of cigarettes consumed per day) and peripheral arterial disease are independent predictors of onychomycosis. Awareness of these factors may help in the prevention of onychomycosis and the optimal management of this disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".